SoroushVahidi/scidocs huggingface dataset:
All your network are belong to us: a transport framework for mobile network selection
(2014)

Mobile devices come with an assortment of networks: WiFi in two different frequency bands, each of which can run in infrastructure-mode, WiFi-Direct mode, or ad hoc mode; cellular radios, which can run in LTE/4G, 3G, or EDGE modes; and Bluetooth. But how should an app choose which network to use? There is no systematic solution to this problem today: in current practice the choice is almost always left to the user, who usually has no idea what's best. In fact, what's best for a user depends on the app's performance objectives (throughput, delay, object load time, etc.) and the user's constraints on cost and battery life. Besides, what's best for a single user or app must be balanced with what's best for the wireless network as a whole (individual optimality vs. social utility). This paper introduces Delphi, a transport-layer module to resolve these issues. Delphi has three noteworthy components: "local learning", in which a mobile device estimates or infers useful properties of different networks efficiently, "property sharing", in which mobile devices share what they learn with other nearby devices, and "selection", in which each node selects a network using what it has observed locally and/or from its neighbors.
Identifying Ghanaian pre-service teachers ’ readiness for computer use : A Technology Acceptance Model approach
(2016)

This study extends the technology acceptance model to identify factors that influence technology acceptance among pre-service teachers in Ghana. Data from 380 usable questionnaires were tested against the research model. Utilising the extended technology acceptance model (TAM) as a research framework, the study found that: pre-service teachers’ pedagogical beliefs, perceived ease of use, perceived usefulness of computer technology and attitude towards computer use to be significant determinants of actual use of computer technology. Results obtained employing multiple stepwise regression analysis revealed that: (1) pre-service teachers’ pedagogical beliefs significantly influenced both perceived ease of use and perceived usefulness, (2) both perceived ease of use and perceived usefulness influence attitude towards computer use and attitude towards computer use significantly influences pre-service teachers’ actual use of computers. However, statistically, perceived ease of use did not significantly influence perceived usefulness. The findings contribute to the literature by validating the TAM in the Ghanaian context and provide several prominent implications for the research and practice of technology integration development.
Community Detection based on Distance Dynamics
(2015)

In this paper, we introduce a new community detection algorithm, called Attractor, which automatically spots communities in a network by examining the changes of "distances" among nodes (i.e. distance dynamics). The fundamental idea is to envision the target network as an adaptive dynamical system, where each node interacts with its neighbors. The interaction will change the distances among nodes, while the distances will affect the interactions. Such interplay eventually leads to a steady distribution of distances, where the nodes sharing the same community move together and the nodes in different communities keep far away from each other. Building upon the distance dynamics, Attractor has several remarkable advantages: (a) It provides an intuitive way to analyze the community structure of a network, and more importantly, faithfully captures the natural communities (with high quality). (b) Attractor allows detecting communities on large-scale networks due to its low time complexity (O(|E|)). (c) Attractor is capable of discovering communities of arbitrary size, and thus small-size communities or anomalies, usually existing in real-world networks, can be well pinpointed. Extensive experiments show that our algorithm allows the effective and efficient community detection and has good performance compared to state-of-the-art algorithms.
Discovering affective regions in deep convolutional neural networks for visual sentiment prediction
(2016)

In this paper, we address the problem of automatically recognizing emotions in still images. While most of current work focus on improving whole-image representations using CNNs, we argue that discovering affective regions and supplementing local features will boost the performance, which is inspired by the observation that both global distributions and salient objects carry massive sentiments. We propose an algorithm to discover affective regions via deep framework, in which we use an off-the-shelf tool to generate N object proposals from a query image and rank these proposals with their objectness scores. Then, each proposal's sentiment score is computed using a pre-trained and fine-tuned CNN model. We combine both scores and select top K regions from the N candidates. These K regions are regarded as the most affective ones of the input image. Finally, we extract deep features from the whole-image and the selected regions, respectively, and sentiment label is predicted. The experiments show that our method is able to detect the affective local regions and achieve state-of-the-art performances on several popular datasets.
Localization & Segmentation of Indian Car Number Plate system : A Projection based Multistage Approach
(2013)

Enormous incorporation of information technologies into all aspects of recent life caused demand for processing vehicles as conceptual resources in information systems. This can be achieved by a human agent, or by special intelligent equipment which is be able to recognize vehicles by their number plates in a real environment and reflect it into conceptual resources. Because of this, various recognition techniques have been developed and number plate recognition systems are today used in various traffic and security applications, such as parking, access and border control, or tracking of stolen cars. This work deals with problematic from field of artificial intelligence, machine vision and neural networks in construction of an automatic car number plate Recognition (ACNPR). This techniques deals with enhanced shadow removal algorithm, along with horizontal and vertical projection method for number plate localization & segmentation..Work comparatively deals with methods achieving invariance of systems towards image illumination, translations and various light conditions during the capture.
Application of CFD in building performance simulation for the outdoor environment : an overview
(2009)

This paper provides an overview of the application of CFD in building performance simulation for the outdoor environment, focused on four topics: (1) pedestrian wind environment around buildings, (2) wind-driven rain on building facades, (3) convective heat transfer coefficients at exterior building surfaces, and (4) air pollutant dispersion around buildings. For each topic, its background, the need for CFD, an overview of some past CFD studies, a discussion about accuracy and some perspectives for practical application are provided. The paper indicates that for all four topics, CFD offers considerable advantages compared to wind tunnel modelling or (semi-)empirical formulae because it can provide detailed whole-flow field data under fully controlled conditions and without similarity constraints. The main limitations are the deficiencies of steady RANS modelling, the increased complexity and computational expense of LES and the requirement of systematic and time-consuming CFD solution verification and validation studies.
Numerical Study of Urban Canyon Microclimate Related to Geometrical Parameters
(2014)

In this study a microclimate analysis on a particular urban configuration: the—street canyon—has been carried out. The analysis, conducted by performing numerical simulations using the finite volumes commercial code ANSYS-Fluent, shows the flow field in an urban environment, taking into account three different aspect ratios (H/W). This analysis can be helpful in the study on urban microclimate and on the heat exchanges with the buildings. Fluid-dynamic fields on vertical planes within the canyon, have been evaluated. The results show the importance of the geometrical configuration, in relation to the ratio between the height (H) of the buildings and the width (W) of the road. This is a very important subject from the point of view of “Smart Cities”, considering the urban canyon as a subsystem of a larger one (the city), which is affected by climate changes.
BEST PRACTICE GUIDELINE FOR THE CFD SIMULATION OF FLOWS IN THE URBAN ENVIRONMENT
(2007)

Legal notice by the COST Office Neither the COST Office nor any person acting on its behalf is responsible for the use which might be made of the information contained in the present publication. The COST Office is not responsible for the external web sites referred to in the present publication. No permission to reproduce or utilize the contents of this book by any means is necessary, other than in the case of images, diagrams or other material from other copyright holders. In such cases permission of the copyright holders is required. This book may be cited as: Title of the book and Action Number
Urban Physics : Effect of the micro-climate on comfort , health and energy demand
(2012)

Y-NC-ND license. Abstract The global trend towards urbanisation explains the growing interest in the study of the modification of the urban climate due to the heat island effect and global warming, and its impact on energy use of buildings. Also urban comfort, health and durability, referring respectively to pedestrian wind/ thermal comfort, pollutant dispersion and wind-driven rain are of interest. Urban Physics is a wellestablished discipline, incorporating relevant branches of physics, environmental chemistry, aerodynamics, meteorology and statistics. Therefore, Urban Physics is well positioned to provide keycontributions to the current urban problems and challenges. The present paper addresses the role of Urban Physics in the study of wind comfort, thermal comfort, energy demand, pollutant dispersion and wind-driven rain. Furthermore, the three major research methods applied in Urban Physics, namely field experiments, wind tunnel experiments and numerical simulations are discussed. Case studies illustrate the current challenges and the relevant contributions of Urban Physics. & 2012. Higher Education Press Limited Company. Production and hosting by Elsevier B.V. Open access under CC BY-NC-ND license.
Verification , Validation , and Predictive Capability in Computational Engineering and Physics
(2002)

Developers of computer codes, analysts who use the codes, and decision makers who rely on the results of the analyses face a critical question: How should confidence in modeling and simulation be critically assessed? Verification and validation (V&V) of computational simulations are the primary methods for building and quantifying this confidence. Briefly, verification is the assessment of the accuracy of the solution to a computational model. Validation is the assessment of the accuracy of a computational simulation by comparison with experimental data. In verification, the relationship of the simulation to the real world is not an issue. In validation, the relationship between computation and the real world, i.e., experimental data, is the issue.
Practical path planning and path following for a non-holonomic mobile robot based on visual servoing
(2016)

In this paper, a practical real-time path planning and robot navigation algorithm for a non-holonomic indoor mobile robot based on visual servoing is implemented. The proposed algorithm is divided into three parts; the first part uses Multi-Stencils Fast Marching (MSFM) as a path planning method. But the generated path results from fast marching methods when used directly, not guaranteed to be safe and smooth. Subsequently, the robot can touch corners, walls and obstacles. The proposed algorithm uses image processing methods to solve this problem. The second part estimates the position and orientation of the robot, from the visual information, to follow the desired path with avoiding obstacles. The third part proposes a decentralized PD-like Fuzzy Logic Controller (FLC) to keep up the robot on the desired path. Experimental results show that the developed design is valid to estimate shortest-path by avoiding obstacles and able to guide the robot to follow the path in real-time.
Analysis of Statistical Question Classification for Fact-Based Questions
(2005)

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.
A Game Theoretic Framework for Incentives in P2P Systems
(2003)

Peer-To-Peer (P2P) networks are self-organizing, distributed systems, with no centralized authority or infrastructure. Because of the voluntary participation, the availability of resources in a P2P system can be highly variable and unpredictable. In this paper, we use ideas from Game Theory to study the interaction of strategic and rational peers, and propose a differential service-based incentive scheme to improve the system’s performance.
Fast and Robust Joint Models for Biomedical Event Extraction
(2011)

Extracting biomedical events from literature has attracted much recent attention. The bestperforming systems so far have been pipelines of simple subtask-specific local classifiers. A natural drawback of such approaches are cascading errors introduced in early stages of the pipeline. We present three joint models of increasing complexity designed to overcome this problem. The first model performs joint trigger and argument extraction, and lends itself to a simple, efficient and exact inference algorithm. The second model captures correlations between events, while the third model ensures consistency between arguments of the same event. Inference in these models is kept tractable through dual decomposition. The first two models outperform the previous best joint approaches and are very competitive with respect to the current state-of-theart. The third model yields the best results reported so far on the BioNLP 2009 shared task, the BioNLP 2011 Genia task and the BioNLP 2011 Infectious Diseases task.
How to improve knowledge transfer strategies and practices in education ? Answers from a systematic literature review
(2010)

Building on the systematic review methodology, this paper aims to examine the knowledge transfer process in education and its main determinants in this specific context. Our findings suggest that linkage agents are central actors in the knowledge transfer process. Their intervention is critical to help adapt the knowledge produced by researchers and make it easier to adopt and use by practitioners. Moreover, the effectiveness of this process hinges on several factors that were broken down into three major categories: determinants related to transferredknowledge attributes, those related to the actors involved in the process, and determinants related to transfer mechanisms.
Laryngeal Tumor Detection and Classification in Endoscopic Video
(2016)

The development of the narrow-band imaging (NBI) has been increasing the interest of medical specialists in the study of laryngeal microvascular network to establish diagnosis without biopsy and pathological examination. A possible solution to this challenging problem is presented in this paper, which proposes an automatic method based on anisotropic filtering and matched filter to extract the lesion area and segment blood vessels. Lesion classification is then performed based on a statistical analysis of the blood vessels' characteristics, such as thickness, tortuosity, and density. Here, the presented algorithm is applied to 50 NBI endoscopic images of laryngeal diseases and the segmentation and classification accuracies are investigated. The experimental results show the proposed algorithm provides reliable results, reaching an overall classification accuracy rating of 84.3%. This is a highly motivating preliminary result that proves the feasibility of the new method and supports the investment in further research and development to translate this study into clinical practice. Furthermore, to our best knowledge, this is the first time image processing is used to automatically classify laryngeal tumors in endoscopic videos based on tumor vascularization characteristics. Therefore, the introduced system represents an innovation in biomedical and health informatics.
Generalized Simulated Annealing
(1996)

We propose a new stochastic algorithm (generalized simulated annealing) for computationally finding the global minimum of a given (not necessarily convex) energy/cost function defined in a continuous D-dimensional space. This algorithm recovers, as particular cases, the so called classical (“Boltzmann machine”) and fast (“Cauchy machine”) simulated annealings, and can be quicker than both. Key-words: Simulated annealing; Nonconvex optimization; Gradient descent; Generalized Statistical Mechanics.
Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement
(2010)

Cross-validation is a mainstay for measuring performance and progress in machine learning. There are subtle differences in how exactly to compute accuracy, F-measure and Area Under the ROC Curve (AUC) in cross-validation studies. However, these details are not discussed in the literature, and incompatible methods are used by various papers and software packages. This leads to inconsistency across the research literature. Anomalies in performance calculations for particular folds and situations go undiscovered when they are buried in aggregated results over many folds and datasets, without ever a person looking at the intermediate performance measurements. This research note clarifies and illustrates the differences, and it provides guidance for how best to measure classification performance under cross-validation. In particular, there are several divergent methods used for computing F-measure, which is often recommended as a performance measure under class imbalance, e.g., for text classification domains and in one-vs.-all reductions of datasets having many classes. We show by experiment that all but one of these computation methods leads to biased measurements, especially under high class imbalance. This paper is of particular interest to those designing machine learning software libraries and researchers focused on high class imbalance.
An Effective High Threating Alarm Mining Method for Cloud Security Management
(2018)

Security equipment such as intrusion prevention system is an important supplementary for security management. They reduce the difficulty of network management by giving alarms corresponding to different attacks instead of raw traffic packet inspection. But there are many false alarms due to their running mechanism, which greatly reduces its usability. In this paper, we develop a hierarchical framework to mine high threating alarms from the massive alarm logs, and aim to provide fundamental and useful information for administrators to design efficient management policy. First, the alarms are divided into two parts based on their attributes, the first part mainly includes several kinds of famous attacks which are critical for security management, we proposed a similar alarm mining method based on Choquet integral to cluster and rank the frequently occurred attacks. The rest alarms constitute the second part, which are caused by the potential threats attacks, also include many false alarms. To reduce the effect of false alarms and rank the potential threats, we employ the frequent pattern mining algorithm to mine correlation rules and then filter false alarms. Following, we proposed a self-adapting threat degree calculation method to qualify the threat degree of these alarms after filtering. To verity the methods developed, an experimental platform is constructed in the campus network of Xi’an Jiaotong University. Experimental results based on the data collected verify the efficiency of the developed methods. For the first kind of alarms, the similar alarms mining accuracy is higher than 97% and the alarms are ranked with different processing urgencies. For the rest alarms, the proposed methods have filtering accuracy above 80% and can rank the potential threats. Based on the ranking results, administrators can deal with the high threats with their limited time and energy, in turn, keep the network under control.
Application of Data Mining Classification Algorithms for Breast Cancer Diagnosis
(2018)

Breast cancer is one of the diseases that represent a large number of incidence and mortality in the world. Data mining classifications techniques will be effective tools for classifying data of cancer to facilitate decision-making.
 The objective of this paper is to compare the performance of different machine learning algorithms in the diagnosis of breast cancer, to define exactly if this type of cancer is a benign or malignant tumor.
 Six machine learning algorithms were evaluated in this research Bayes Network (BN), Support Vector Machine (SVM), k-nearest neighbors algorithm (Knn), Artificial Neural Network (ANN), Decision Tree (C4.5) and Logistic Regression. The simulation of the algorithms is done using the WEKA tool (The Waikato Environment for Knowledge Analysis) on the Wisconsin breast cancer dataset available in UCI machine learning repository.
An Unsupervised Clustering Tool for Unstructured Data
(1999)

We present an unsupervised clustering tool, Principal Direction Divisive Partitioning, which is a scal-able and versatile top-down method applicable to any set of data that can be represented as numerical vectors. A description of the basic method, a summary of the main application areas where this has been used, and some recent results on the selection of signiicant words as well as the process of updating clusters as new data arrives is discussed.
Basic studies on wet adhesion system for wall climbing robots
(2007)

This paper reports a vacuum-based wet adhesion system for wall climbing robots. In this adhesion system, a suction cup adheres on a wet surface. The problems addressed are an adherability on a rough surface, which is comes from the seal action of a liquid, and low friction between suction cup and adhered rough and smooth surfaces which is comes form lubricating action of a liquid. Generally, it is difficult that a vacuumed suction cup adheres on rough surface such concrete plate and hardly slidable. In this paper, the adhesion force and friction when a suction cup adheres on smooth glass plate and concrete plate are measured and compared wet condition with dry condition. The experiment result showed that a viscosity is important at the sealing performance of adhesion on rough surface. The silicon oil of a viscosity of 5000cSt allows a suction cup to adhere on concrete surface. In this condition it comes up to the adhesion force when a suction cup adheres on smooth glass with dry condition.